INDEX, MIDDLE, AND RING FINGER EXTRACTION AND

IDENTIFICATION BY INDEX, MIDDLE, AND

RING FINGER OUTLINES

Ching-Liang Su

Department of Industrial Engineering and Technology Management, Da Yeh University

112 Shan-Jiau Road, Da-Tsuen, Chang-Hua, Taiwan 51505, China

Keywords: Finger Shape Identification, Finger Shape Extraction, Locating Fingertip and Finger-to-Finger-Valley,

Geometry Matching.

Abstract: In this study, the new technique is used to extract the index, middle and ring finger outlines. The

orientations and geometrical features of these outlines are calculated and compared to identify different

individuals. The techniques of database SQL searching and manipulation, image dilation, object position

locating, image shifting, rotation, and interpolation are used to recognize different individuals. The hand

was fixed each time when a photograph was taken, and one can assume that each time when a hand was

acquired, the image was the same as the previous one. Since the photographs are the same, after the index,

middle or ring fingers have been extracted from the hand image, the acquired images can be used to identify

different persons.

1 INTRODUCTION

In this study, the new technique is used to extract the

index, middle, and ring finger-outlines and to

perform the person’s identification. The image

automatic registration technique locates the

orientations and positions of the extracted finger

images. In order to perform the geometrical

comparison of two finger images, the centroids of

the finger images need to be located. The images

used in this study are the 128 by 128 images. One

needs to perform the image movement to move the

image to the center of the frame. Furthermore, the

major axes of the finger images need to be located

and we need to move the images to allow the major

axes of the finger images to be in the straight

position. Since every image is centered and

straightened, one can perform the image geometrical

comparison to identify different fingers. The above

steps are performed by computer itself and no further

human involvement are required. The algorithm

developed in this study can identify fingers.

2 ACQUIRING HAND IMAGE

Figure 1 shows the acquired hand images. After

further processing, one can obtain the hand-edge

images. By the edge thinning direction and the hand

geometry features, one can extract the fingertips and

finger-to-finger-valleys, as shown in figure 2.

3 EXTRACTING INDEX,

MIDDLE, AND RING FINGERS

After the algorithm locate the locations of the

fingertips and finger-to-finger valleys, one can

extract the index, middle, and ring finger. The

extracted results are shown in figures 3, 4, and 5,

respectively. After further processing one can obtain

the fully closed finger images.

Figure 1: Acquired hand images.

518

Su C. (2008).

INDEX, MIDDLE, AND RING FINGER EXTRACTION AND IDENTIFICATION BY INDEX, MIDDLE, AND RING FINGER OUTLINES.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 518-520

DOI: 10.5220/0001079505180520

 SciTePress

Figure 2: Extracted fingertips and finger-to-finger valleys.

4 IMAGE REGISTRATION AND

SUBTRACTION

In order to perform the comparison of two fingers,

the centroids of the finger images need to be

determined. The images used in this study are the

128 by 128 frames. One needs to perform the image

movement to move image to the center of the 128 by

128 frame; i.e. one needs to shift image to allow the

centroid of each image to be in location (64,64) of

the frame. Furthermore, major axes of fingers need

to be located and we need to move fingers to allow

major axes of the fingers to be aligned to the same

straight position. Since every image is shifted to the

center of the 128 by 128 frame and the major axis of

each image is aligned to the same vertical position,

one can perform the image subtraction to determine

the differences of each two fingers.

After the image movement, as mentioned

previously, in the new feature-domain, every finger

image would be aligned to the same straight and

center position.

After image rotating, shifting, and interpolating,

two finger images overlap. Since both fingers

overlap, image subtraction can now be applied to

compare the difference of these two fingers. By the

subtracted results, one can identify different fingers.

Figure 6 shows the image subtractions of index

fingers. The subtracted-data of the index fingers is

shown in table 1.

5 RESULTS AND CONCLUSIONS

Table 1 shows the partial comparison data on

differences after performing image subtraction. The

data inside the rectangular boxes in the table are the

results of subtraction for genuine index finger. The

other data in this table comprise the subtracted

results from two different index fingers. By

analyzing the date in table 1, one can conclude that

the accuracy rate is 97%.

ACKNOWLEDGEMENTS

The National Science Council, Taiwan, supported

this work under grant NSC 96-2221-E-212-004.

REFERENCES

Miguel A. Ferrer, Carlos M. Travieso, and Jesus B.

Alouso, “Using hand knuckle texture for biometric

identifications,” IEEE A&E Systems Magazine, June

2006

Sotiris Malassiotis, Niki Aifanti, and Michael G. Strintzis,

“Personal Authentication Using 3-D Finger

Geometry,” IEEE Transactions on Information

Forensics and Security, vol. 1, no. 1, March 2006

Erdem Yörük, Ender Konuko˘Glu, and Bülent Sankur,

“Shape-based hand recognition,” IEEE Transactions

on Image Processing, vol. 15, no. 7, July 2006

Figure 3: Extracted index fingers.

Figure 4: Extracted middle fingers.

INDEX, MIDDLE, AND RING FINGER EXTRACTION AND IDENTIFICATION BY INDEX, MIDDLE, AND RING

FINGER OUTLINES

519

Figure 5: Extracted ring fingers.

Figure 6: Index finger subtraction.

Table 1: Comparison-data of index fingers.

Comparisons of same

person’s index fingers

13606 9344 32648 35920 32057 19731 19951 20553 21518 22666 24366 23012 23208 24767

9075 36050 39984 35514 16802 18862 17742 27583 27046 25050 19072 19944 23275

32330 36490 31081 17812 20023 17886 21973 22145 22144 21070 21091 23797

15355 6971 39351 40742 41120 27115 28159 34670 42610 45765 41568

14295 45178 45510 47317 29593 29584 34317 45483 49224 43513

39852 41553 40999 27351 26778 32646 42803 45542 42197

8055 11227 28340 28836 28194 12836 12500 17419

11585 29746 31098 30431 14746 15300 18198

30076 31020 30898 16496 12981 20415

11496 18355 32092 34529 31320

14401 32070 34369 31665

28564 31604 27848

10206 11121

14594

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